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  • 1.
    Ali Hamad, Rebeen
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Lundström, Jens
    JeCom Consulting, Halmstad, Sweden.
    Stability analysis of the t-SNE algorithm for human activity pattern data2018Conference paper (Refereed)
    Abstract [en]

    Health technological systems learning from and reacting on how humans behave in sensor equipped environments are today being commercialized. These systems rely on the assumptions that training data and testing data share the same feature space, and residing from the same underlying distribution - which is commonly unrealistic in real-world applications. Instead, the use of transfer learning could be considered. In order to transfer knowledge between a source and a target domain these should be mapped to a common latent feature space. In this work, the dimensionality reduction algorithm t-SNE is used to map data to a similar feature space and is further investigated through a proposed novel analysis of output stability. The proposed analysis, Normalized Linear Procrustes Analysis (NLPA) extends the existing Procrustes and Local Procrustes algorithms for aligning manifolds. The methods are tested on data reflecting human behaviour patterns from data collected in a smart home environment. Results show high partial output stability for the t-SNE algorithm for the tested input data for which NLPA is able to detect clusters which are individually aligned and compared. The results highlight the importance of understanding output stability before incorporating dimensionality reduction algorithms into further computation, e.g. for transfer learning.

  • 2.
    Ali Hamad, Rebeen
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Salguero Hidalgo, Alberto
    University of Cádiz, Cádiz, Spain.
    Bouguelia, Mohamed-Rafik
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Estevez, Macarena Espinilla
    University of Jaén, Jaén, Spain.
    Quero, Javier Medina
    University of Jaén, Jaén, Spain.
    Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors2020In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 24, no 2, p. 387-395Article in journal (Refereed)
    Abstract [en]

    Human activity recognition has become an activeresearch field over the past few years due to its wide applicationin various fields such as health-care, smart homemonitoring, and surveillance. Existing approaches for activityrecognition in smart homes have achieved promisingresults. Most of these approaches evaluate real-timerecognition of activities using only sensor activations thatprecede the evaluation time (where the decision is made).However, in several critical situations, such as diagnosingpeople with dementia, “preceding sensor activations”are not always sufficient to accurately recognize theinhabitant’s daily activities in each evaluated time. Toimprove performance, we propose a method that delaysthe recognition process in order to include some sensoractivations that occur after the point in time where thedecision needs to be made. For this, the proposed methoduses multiple incremental fuzzy temporal windows toextract features from both preceding and some oncomingsensor activations. The proposed method is evaluated withtwo temporal deep learning models (convolutional neuralnetwork and long short-term memory), on a binary sensordataset of real daily living activities. The experimentalevaluation shows that the proposed method achievessignificantly better results than the real-time approach,and that the representation with fuzzy temporal windowsenhances performance within deep learning models. © Copyright 2020 IEEE

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